The Internet provides access to a wide variety of content. For instance, images, audio, video, and web pages for a myriad of different topics are accessible through the Internet. The accessible content provides an opportunity to place advertisements. Advertisements can be placed within content, such as a web page, image or video, or the content can trigger the display of one or more advertisements, such as presenting an advertisement in an advertisement slot.
Advertisers decide which ads are displayed within particular content using various advertising management tools. These tools also allow an advertiser to track the performance of various ads or ad campaigns. The parameters used to determine when to display a particular ad can also be changed using advertising management tools.
The data that is used to generate the performance measures for the advertiser generally includes all data that is available. This data usually includes a combination of data from multiple servers. The amount of the combined data is large enough that performance measures generated from the data can be used to provide an efficient way of understanding the data. Processing of the data to generate useful and accurate performance measures involves a number of obstacles. For instance, if a performance measure is based upon a user's actions over a period of time, the user's actions should be tracked. The user's action may be tracked using a variety of implementations, such as but not limited to, a cookie, server log files, profiles or other suitable ways of tracking the user interaction. A cookie can be used to track a user's actions over a period of time. However, if this cookie is removed during the period of time, collection of accurate data tracking the user's actions may be disrupted. The data can contain recorded user actions and an advertiser can specify which of these actions should be considered a conversion. The conversions can be any recordable events that meet an advertiser's predetermined criteria. Identifying other actions that contribute to the occurrence of conversions is valuable. The data, however, contains numerous actions that could be associated with conversions. In addition, the data may also contain information regarding user actions that do not contribute to any recorded conversions. Thus, processing the data to provide accurate and reliable performance measures based upon as much information as possible regarding user actions has a number of challenges. For example, a user can visit 10 pages, put two things in their cart, and finally complete a purchase. The data could potentially contain all of this information (and more), but an advertiser may choose to consider a purchase as a conversion. Other advertisers may say that adding to the cart is a type of conversion, as is the purchase themselves. Additionally or alternatively other advertisers may say that staying on a page for 30 seconds or longer is a type of a conversion.